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- W3132868889 abstract "Understanding how life works requires the study of biochemical reactions in vivo on a single-molecule level, which typically involves monitoring molecular species and how they interconvert to each other. These studies should also be conducted with freely diffusing molecules since diffusion is essential in important processes such as transcription factors finding sites on a DNA. Our goal is to learn about the biochemical reaction rates and some diffusion dynamics of biomolecules at a single-photon level. Previous lab efforts have been focused on probing the number of molecular species, simultaneously and self-consistently, via directly conducting a photon-by-photon analysis on the experimental data in a nonparametric fashion without presuming the total number of chemical species. Here, we would like to go beyond this by taking transformations into account as well, and we will do so by leveraging the novel tool of Bayesian nonparametrics (BNP) that are now computationally feasible based on the methods from computational statistics that we are exploiting in the lab, including the Hamiltonian Monte Carlo (HMC), sequential Bayesian filtering, etc. Our approach has been validated using some simulated data containing roughly several thousand of photon arrival times, and it will soon be tested against the experimental data we have obtained recently." @default.
- W3132868889 created "2021-03-01" @default.
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- W3132868889 date "2021-02-01" @default.
- W3132868889 modified "2023-09-25" @default.
- W3132868889 title "A Bayesian Nonparametric Approach to Learning Molecular Species and Diffusion Dynamics Based on Photon Arrival Data" @default.
- W3132868889 doi "https://doi.org/10.1016/j.bpj.2020.11.1276" @default.
- W3132868889 hasPublicationYear "2021" @default.
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